"In this article, it have discussed three waves of quantum machine learning, each harnessing a particular aspect of quantum computers and targeting particular problems. The first scrutinizes the power of quantum computers to work with high-dimensional data and speed-up algebra, but raises the caveat of input/output due to the quantum measurement rules. The second domain circumvents this problem by using a hybrid architecture, performing optimization on a classical computer while evaluating parameterized states on a quantum circuit, chosen based on a particular problem. Finally, the third domain is inspired by brain-like computation and uses the natural interaction and unitary dynamic of a given quantum system as a source for learning." shorturl.at/ackU5

“One of the key obstacles to accurate quantum simulations is noise—random errors in both the switching of the “gates” that perform quantum logic operations and in the reading of their output states. These errors accumulate and restrict the number of gate operations a computation can enact before the noise dominates. The researchers found that simulations with more than 300 gates were overwhelmed by noise. But the more complex the system, the more gates are needed.” shorturl.at/kpLPW

"But a century ago, the pioneers of quantum mechanics made a surprising discovery — one that elevated unitarity from common sense to a hallowed principle. The surprise was that, mathematically, the quantum world operates not by probabilities but by more complicated numbers known as amplitudes. An amplitude is essentially the degree to which a particle is in a certain state; it can be a positive, negative or imaginary number. To calculate the probability of actually observing a particle in a certain state, physicists square the amplitude (or, if the amplitude is an imaginary number, they square its absolute value), which gets rid of the imaginary and negative bits and produces a positive probability. Unitarity says the sum of these probabilities (really, the squares of all the amplitudes) must equal 1." https://bit.ly/3gF70DT

“It can be easy to take math for granted. But the field has evolved over millennia, and concepts that seem obvious today had to be invented. Although zero is the beginning of counting, it arrived late to the math party. But not as late as its counterpart, infinity. Four thought experiments featured in NOVA’s “Zero to Infinity” illustrate how people used the world around them to explore these revolutionary concepts.” https://to.pbs.org/3XSqS78

Experimental Entropy Analysis Over Quantum NN´s & IRL Agent (Lab1-Exp3).

In this experiment we focus on one of the properties of information that says that as the amount of available information increases, uncertainty decreases. To increase the amount of information available in our scenario we are going to use the LZC algorithm. The reward function is built from the behavior of the market using different synthetic signals (i.e. Quantum Signal) to establish policies and be able to act "optimally".

Link Dashboard-> https://bit.ly/3UD2ynb

4 Highlights from IRL Agent (Datasets until 26/Nov/2022):
-Detect High Volatility (Last 4 Weeks)
-The relation skewness & Average Price is very low
-The probability of transition to the 26K-38K price bins is below 0.07
-Buy probability is below 0.08

*Comments: Recall this CloudLab its just only for experimental purpose, the main objective is not to give a financial advice but give some experimental perspectives.

*Report: Next experiment Nov/2023 (Lab1-Exp4)

Who was Marvin Minsky - this is an essay written by chatgpt - https://open.substack.com/pub/vzocca/p/who-was-marvin-minsky-by-chatgpt?r=1nqhf3&utm_medium=ios&utm_campaign=post

Let’s finish the year on a light note: chocolate consumption and Nobel laureates correlation - https://www.sciencedirect.com/science/article/pii/S2590291120300711

Is the AI hype just a lie? - https://www.youtube.com/watch?v=PBdZi_JtV4c&t=5s

In 2022, mathematicians solved a centuries-old geometry question, proved the best way to minimize the surface area of clusters of up to five bubbles and proved a sweeping statement about how structure emerges in random sets and graphs. https://bit.ly/3GaaVRE

From this channel we want to wish you all a Happy 2023,that the next year be full of great opportunities, challenges, creativity, experiments and adventures...

Quantum Complexity Tamed by Machine Learning

If scientists understood exactly how electrons act in molecules, they’d be able to predict the behavior of everything from experimental drugs to high-temperature superconductors. Following decades of physics-based insights, artificial intelligence systems are taking the next leap. https://bit.ly/3GAR8LC

“As quantum computing attracts more attention and funding, Aaronson says, researchers may mislead investors, government agencies, journalists, the public and, worst of all, themselves about their work’s potential. If researchers can’t keep their promises, excitement might give way to doubt, disappointment and anger, Aaronson warns. The field might lose funding and talent and lapse into a quantum-computer “winter” like those that have plagued artificial intelligence.” https://bit.ly/3kuzBNK

“Rotational invariance is a symmetry exhibited by the circle: Rotate it any number of degrees and it looks the same. In the context of physical systems on the brink of phase changes, it means many properties of the system behave the same regardless of how a model of the system is rotated.” https://bit.ly/3HkwjnX

“In 2020, two researchers at the Massachusetts Institute of Technology led a team that introduced a new kind of neural network based on real-life intelligence — but not our own. Instead, they took inspiration from the tiny roundworm, Caenorhabditis elegans, to produce what they called liquid neural networks. After a breakthrough last year, the novel networks may now be versatile enough to supplant their traditional counterparts for certain applications.”

“Liquid networks also differ in how they treat synapses, the connections between artificial neurons. The strength of those connections in a standard neural network can be expressed by a single number, its weight. In liquid networks, the exchange of signals between neurons is a probabilistic process governed by a “nonlinear” function, meaning that responses to inputs are not always proportional. A doubling of the input, for instance, could lead to a much bigger or smaller shift in the output. This built-in variability is why the networks are called “liquid.” The way a neuron reacts can vary depending on the input it receives.” https://bit.ly/3DRwWEC

“Imagine you had some useful knowledge — maybe a secret recipe, or the key to a cipher. Could you prove to a friend that you had that knowledge, without revealing anything about it? Computer scientists proved over 30 years ago that you could, if you used what’s called a zero-knowledge proof” https://bit.ly/3IyMSyi

“The quantum energy teleportation protocol was proposed in 2008 and largely ignored. Now two independent experiments have shown that it works.”
“Now in the past year, researchers have teleported energy across microscopic distances in two separate quantum devices, vindicating Hotta’s theory. The research leaves little room for doubt that energy teleportation is a genuine quantum phenomenon.” https://bit.ly/3Zirini

Do we have a conducive environment for Quantum Machine Learning?
There are several software packages and platforms that provide environments for quantum machine learning, including:

Qiskit: An open-source quantum computing framework for writing, running, and debugging quantum programmes. It includes tools for working with quantum circuits and algorithms as well as a variety of quantum simulators and quantum hardware backends.

IBM Quantum Experience: A cloud-based platform for accessing IBM’s quantum computers and experimenting with quantum algorithms. It includes tools for writing and running quantum programmes, as well as a variety of quantum simulators and quantum hardware backends.
https://bit.ly/3KVIJGl

Physicists have coaxed particles of light into undergoing opposite transformations simultaneously, like a human turning into a werewolf as the werewolf turns into a human. In carefully engineered circuits, the photons act as if time were flowing in a quantum combination of forward and backward…. https://bit.ly/3Fg6J3l

“Boston Dynamics’ Atlas robot over the years progressed from unstable approaches to a set of stairs to the equivalent of human parcours and dancing by leveraging ever more sophisticated machine learning. The device autonomously vacuuming your carpets demonstrated progression over time, where they now continuously map their environments then estimate their location within that environment in order to move. Robot vacuums rely on simultaneous localisation and mapping (SLAM) algorithms, which innovatively have been crossed with the quantum qubit. A University of Sydneyteam created an “adaptive algorithm that measures the performance of one qubit and uses that information to estimate the capabilities of nearby qubits…called Noise Mapping for Quantum Architectures. Instead of considering the singular environment of one qubit, the team automated and sped up the process by reducing the number of measurements and qubits required making quantum computing more effective from traditional robot-inspired techniques.” https://bit.ly/40dVfpD

Should we stop large AI systems from being developed further? Read the open letter from prominent AI experts requesting a stop of 6 months - https://futureoflife.org/open-letter/pause-giant-ai-experiments/